1. The Field of the Invention
The present invention relates generally to a system and method for optimizing prices for products.
2. Description of Related Art
Sellers have often relied on price optimization techniques to assist in establishing the optimal pricing of products in order to achieve established business objectives. Price optimization typically involves several different steps. The first step of price optimization may involve gathering demand data for a product from a database containing historical sales information. Demand data may include both demand, e.g., volume of units sold, and demand factors, e.g., price, promotions, seasonal information, etc. Once a sufficient amount of demand data has been gathered, the next step involves quantifying any relationships or trends between the demand and the demand factors. This may involve applying statistical methods, sometimes referred to as regression analysis, for modeling the demand of a product as a function of its associated demand factors. The goal of the modeling is to determine the values of parameters that “best fit” the demand factors to the demand. The next step of price optimization involves using the values of the parameters determined in the previous step to determine a solution to one or more price optimization equations. The solution to the price optimization equations may correspond to an optimal price for a product. The optimal price may then be used to define a listing price for the product.
The increased proliferation of transactions for products and services over electronic networks, such as the Internet, has allowed for the increased automation of price optimization techniques. These techniques allow the pricing of products listed on an e-commerce website to be adjusted monthly, weekly, daily or even hourly to maintain optimal prices in light of current market conditions and ever-changing business objectives. Indeed, due to the highly competitive on-line sales environment, the success of an e-commerce website may be determined in large part by its ability to quickly and optimally price its products.
Despite their increased importance to the success of sellers in the marketplace, presently available price optimization techniques have fallen short in their ability to meet some of the business objectives dictated by sellers. One shortcoming of presently available price optimization techniques is the inability to take into account some of the indirect benefits related to listing a particular product for sale. These indirect benefits may include the ability of a product to increase the sales of other products listed on an e-commerce website by the mere presence of the product on the website. In this type of a situation, it may be undesirable to sell out of the product too soon.
Unfortunately, currently available price optimization techniques may allow an inventory of the product to be exhausted too soon, thereby causing the loss of the indirect benefits provided by the availability of the product. It would therefore be an improvement over the presently available price optimization techniques to define an optimal price for a product that is predicted not to cause an inventory of the product to be exhausted during a time interval established by the seller.
Another shortcoming of the presently available price optimization techniques is the inability to establish reliable pricing for products that have limited or no historical sales information. For example, using presently available price optimization techniques, it may be difficult to define an optimal price for a product due to the lack of sufficient demand data to reliably perform a regression analysis. Some attempts have been previously made to overcome the lack of sufficient historical sales information for a product. These attempts have included applying clustering techniques to a group of products to thereby determine clusters of analogous products. A product with limited historical sales information was then associated to one of the product clusters. The historical sales information for the products in the associated cluster was then utilized in a price optimization technique to thereby determine an optimal price for the product with the limited historical sales information. However, while a step in the right direction, the past techniques for determining the similarity between products for clustering purposes have often produced unsatisfactory and unreliable results. It would therefore be an improvement over the presently available techniques to more accurately determine whether products are in fact analogous for clustering purposes.
Another shortcoming of the presently available price optimization techniques is the inability to establish reliable price elasticity models in the absence of complete historical promotions data. In particular, where past promotions data for a product is absent, it is difficult to predict the effect that changes in the price of a product will have on the demand for the product. Therefore, there exists a need for accounting for future promotions on the price elasticity of a product in the absence of historic promotions data.
The previously available art is thus characterized by several disadvantages that are addressed by certain embodiments of the present invention. Embodiments of the present invention minimize, and in some aspects eliminate, the above-mentioned failures, and other problems, by utilizing the methods and structural features described herein. The features and advantages of the invention will be set forth in the following description, and in part will be apparent from the description, or may be learned by the practice of the invention without undue experimentation. The features and advantages of the invention may be realized and obtained by means of the instruments and combinations particularly pointed out in the appended claims.